FPXRF--偏最小二乘法定量分析土壤中的铅含量

Analysis of Lead in Soil with Partial Least Square Regression(PLS) Method and Field Portable X-Ray Fluorescence(FPXRF)Analyzer

在实验室条件下,利用NITON XLt920型便携式X射线荧光光谱(field portable X-ray fluorescence,FPXRF)仪获取土壤样品的X射线荧光光谱数据,并采用偏最小二乘法(PLS)建立土壤Pb含量的预测模型。模型所用的光谱范围为与土壤中Pb元素密切相关的两个波段:10.40～10.70keV和12.41～12.80keV;最佳主成分数为6。模型经交互验证,其预测结果与实测值之间的相关系数为0.9666,预测均方根误差(RM-SEP)为0.8732。另外为了与偏最小二乘法做比较,还分别利用仪器直接获取的Pb含量读数以及X射线荧光光谱数据中Pb的Lα和Lβ线的强度与ICP测定值进行一元线性和多元线性回归,相关系数分别为0.6805和0.7302,均低于PLS模型的预测结果。研究表明,相比较传统的原子吸收等测试方法,便携式XRF仪在保证一定测试精度基础上,具有方便、快速、无损和耗费少等优势,可作为进一步分析前有力的筛选手段。

In the present study, soil samples were scanned by NITON XLt920 field portable X-ray fluorescence （FPXRF）analy zer, and the relationship between the X-ray fluorescence spectra and the concentration of Pb in soil was studied. For predicating the Pb concentration in soil, a partial least square regression model （PLS）was established with 6 optimal factors and two closely relevant electron volt ranges： 10. 40-10. 70 keV and 12.41-12.80 keV. After cross-calibration, the correlation coefficient of value predicted by PLS model against that measured by ICP was 0. 966 6, and the root mean square error of prediction （RMSEP）was 0. 873 2. Meanwhile, the univariate linear regression and multivariate linear regression models were also built with the correla- tion coefficient of 0. 680 5 and 0. 730 2, respectively. Obviously, the PLS method was better than the other two methods for predication. Comparing to the conventional approach of atomic absorption spectroscopy（AAS）, FPXRF has the advantages of rapidness, non-destruction and relatively low cost with the acceptable accuracy. It would be a powerful tool to decide which sample is needs for further analysis.